Reinvent Onboarding With Personalization

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Getting started in direct selling is exciting. The new enrollee is on the receiving end of intense focus by sponsors and the company, but that quickly passes for practical and understandable business reasons. Then, the new distributor too often is left on their own with no roadmap for success. Personalized mobile apps can step in to extend the one-on-one onboarding experience.

During the initial days in a network, a sponsor may talk with the enrollee daily. Learning must deliver the enrollee a sense of purpose to ground new distributors in company mission, policy, and how they will be measured for success. In those first days, there are many policies to remember and sales actions to start growing the funnel of prospects. But within days, sponsors are on to their next recruit and often leave an enrollee to fend for themselves, long before the Society of Human Resources Management’s recommended minimum of 90 days of onboarding.

With an app in hand, enrollees can dig into a library of company learning, be prompted to start sales activities, and receive feedback and recognition based on their success. These activities can be monitored and reported to sponsors or management, allowing them to reach out with encouragement and individualized next-step guidance with a single tap.

Reinvent your onboarding experience by taking the time to associate content and sales process steps to measurable events in a smart sales platform.

A smart sales content platform can shepherd new distributors and provide reinforcement of learning and company best practice with automated recognition messaging. Sponsors can also receive updates about their enrollee’s activities and reach out when it will make the biggest difference, saving them time while accelerating time-to-first sale by their newest team members.

Assembling An Onboarding Library

When breaking down existing training content for ingestion into a sales automation platform, a crucial step in a company’s adoption of smart tools, content should be linked to identified next actions the new distributor can take to become successful using brand content and processes. Early users of the Gig Economy Group-based LifeVantage App describe the resulting experience as “like having a personal secretary reminding you to follow-up,” according to Jacqueline, a reviewer on the Apple App Store.

Begin by separating basic company knowledge, product introductions and product knowledge sequences, as well as initial sales actions and related sales skills videos. Address each group of content assets separately, always thinking about where in their onboarding the enrollee will engage with each type of content.

“Great job” is the most potent phrase for extending onboarding, and your company can send that message whenever a recent enrollee takes an important step. Action cards, such as the content sharing recommendation to the left, can be triggered based on distributor actions, prospect behavior, or company policy, giving guidance and encouragement.

Action cards provide suggested content and messaging to distributors, which they are free to accept, reject, or modify. GEG’s platform tracks and learns what works best.

Gig Economy Group’s action card interface, for example, can be configured to respond to activity with recognition messages (“Attaboy!”), related information, or the what’s next activity necessary to progress. Recognition, in particular, should be tied to:

  • Viewing all of a video or an entire sequence of videos;
  • Adding a contact;
  • Sharing content or sending a message to a prospect;
  • Following up with a prospect after a sales step, such as sharing media or a shopping cart, and, of course;
  • Converting a sale or getting a prospect to increase their product interest.

The same triggers can be set to send the enrollee’s sponsor or a sales team member a message alerting them to how the onboarding is going.

As your automated onboarding evolves, take the time to return to the management interface to add new tracked events, such as a distributor’s lack of activity, difficulty moving a prospect along the funnel (signaled by repeated sharing without any change in prospect interest level), or positive results to spur the sponsor to communicate.

Reinforcing action, which more than 80 percent of new distributors fail to take, can dramatically improve early sales success. It’s also important to allow new distributors to decide for themselves.

“I love that you can delete suggestions [in action cards] since they aren’t always the right choice for a particular person,” wrote one Apple App Store reviewer, JudiPP, of the LifeVantage app.

When onboarding, and throughout the distributor’s relationship with a company, people want to know what to do next, not take arbitrary orders. Allowing people to experiment with their selling style is essential to their sense of efficacy, and the variations they introduce into the process is fodder for the smart platform to learn.

Sales and marketing leaders can use existing content and new data-targetted content production to create a genuinely inviting onboarding experience that creates a conversation between enrollees and their sponsors long after traditional welcome activities end. Well placed triggers in the onboarding and daily sales process can alert management and sponsors to distributors in need of help, or just a push toward activity.

 

How Salespeople Can Start Selling On Day One

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Helping a new distributor during the “golden two weeks,” when those enrollees who close their first sales or distributor enrollments are most likely to become a high-earning, long-term member of a direct selling network, is the best onboarding investment. Bar none. It moves the potential sales rep toward confidently repeating the company’s sales process. Getting new enrollees to “work the system” from Day One with an organization creates the bond that drives network growth and improved revenue.

Distributors who start sales activities and close sales within 10 business days of enrollment will earn 71 percent more than a peer who takes just two weeks longer to make their first commission, an analysis of nine years of sales data by LifeVantage found. Direct selling trainer ServiceQuest reports that a 10 percent increase in distributor retention will produce 49 percent more revenue over 10 years compared to unengaged distributors.

The Gig Economy Group (GEG) platform and app eliminate all tool-centric training, providing easily understood functions to do one action at a time.

Machine learning tools can coach a newbie from their first moments with a direct selling company, but the most important action automation can facilitate is the adding of new prospects, initial messaging to those prospects, an established pattern for follow-ups and content sharing to build the prospect’s confidence in the salesperson, the company, and the trust relationship that will result in ongoing sales and auto-ship registrations.

McKinsey concluded that sales and marketing uses of Artificial Intelligence — the catch-all description that includes machine learning — will produce $1.4 Trillion to $2.6 Trillion in improved sales and marketing performance, with more than two-thirds of the value coming from enhancement of existing analytics. Your sales process, if mapped as part of machine learning adoption, is the raw material needed to increase revenue and retention.

The problem, or rather the reality is that 80 percent of new distributors never take any action. They either fail to take any action or get bogged down in trying to understand the company and the products or services they’ve signed up to sell. Without sales actions, there is no data to use when optimizing sales procedures.

First and foremost, direct selling companies must get new enrollees to start adding and working prospect relationships.

What’s Next is Step One

Focus new distributors on two necessary goals on their first day: 1.) Understanding their new company’s values, and; 2.) Adding and reaching out to their first prospects.

We’ve discussed how to map your onboarding process here. Let’s concentrate on the problem of getting people to act. Throughout any guided experience, whether it is delivering sales coaching or interpreting marketing data to suggest better selling steps, the “What’s Next” approach to app user experience is the most effective means of getting people to move through a sequence of activities to achieve a goal. During the first two weeks with a company, new distributors remain unsure about the company and its mission or processes.

A LifeVantage App action card suggests a video to share with a new prospect based on their interest, and over the next two days will remind the distributor to follow-up, along with the appropriate content so share next.

Onboarding content that provides a clear, concise narrative about the values and mission of the company sets the stage for action. Then, the barrier becomes the complexity of the tools themselves.

Too often, apps require users to learn many tool skills and go about it by walking through many steps before allowing people to start using the tool for its primary purpose, such as adding and communicating with a new prospect. As apps grow more sophisticated, these learning processes become more complex, raising barriers to success for the distributor who needs to do simple steps in the simplest way possible. Consider the vast breadth of capabilities of Microsoft Word or Adobe Photoshop, which most users never need and will not explore without a specific context, getting their job done.

Artificial Intelligence apps have to stay focused on the human actions they support, hiding all complexity that will prevent an aspiring distributor from taking the actions necessary to close their first sale. The Gig Economy Group (GEG) platform and app eliminate all tool-centric training, providing easily understood functions to do one action at a time. For example, on their first day, a distributor is asked to enter one or more new contacts. There are no elaborate instructions, just an “action card” that suggests what to do and, with a tap of a button, the tool to do it in the simplest form possible.

But data entry is not the salesperson’s main interest or a reason to be enthusiastic about their first day on the job. The GEG platform ingests the new contact data, reviews the information, and immediately suggests recommended messaging and content to share in order to start the prospect conversation. After the distributor sends their first outreach message to a prospect, the platform monitors whether the content has been viewed, as well as any responses sent by the prospect, so that it can coach the new enrollee toward the sale.

For example, in the GEG-based LifeVantage App, the action card (see image to the right) is generated in response to a new contact entered in the app. Assessing the prospect interests entered (or not entered) by the distributor, the platform suggests a specific video program to share with the contact to begin the conversation. If the distributor accepts the recommended action, the app delivers suggested text to use when sharing the video in the next screen, which is part of the messaging toolset. But the distributor’s experience remains focused on their next step in the relationship rather than navigating between different tools.

In this case, AI smooths the technological overhead of a complex set of application capabilities, leaving sellers to emphasize their strengths, which are developing relationships, choosing the right words, and delivering the information a customer needs at exactly the right time. At the end of Day One, the distributor has seen three short onboarding videos and has at least one, if not the recommended five, prospects in motion. Those actions translate into commissions, which keep distributors engaged and eager to grow their business.

Selling is hard work. Make it easier for new enrollees to concentrate on their strengths instead of the tools they must use to grow their personal funnel and move prospects toward the close. What’s next should always be related to the state of the distributor-prospect relationship, not the distributor’s competence with a set of digital tools.

Mapping The Sales Journey: Machine Optimizing Customer Experience

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In the previous posting, we explained how to break down a content in an existing digital asset library into major categories, Onboarding, Prospect Development, Product Knowledge Development, and Sales Skills Improvement programming. Each of these phases of the distributor and customer journey must be inventoried and the expected outcomes to be produced by each asset identified.

As a team begins to use machine learning, the next step is to focus on the customer journey in its Prospect Development content, because it has the greatest influence on conversion rates and revenue. These assets provide a machine learner with measurable steps in a customer journey and the team’s job is selecting what to measure at each customer touchpoint.

Focus on the process of moving a prospect from initial awareness into a distributor relationship or to the close of a product sale. Set aside the social content used to attract awareness along with the content used to train and inform distributors right now. The video, articles, product sheets, and other materials shared between a distributor and a prospect are the only concern at this step. Don’t be afraid to throw out content that doesn’t fit and to plan the production of new content that may work better.

Consider each content asset as though it is a candidate when hiring a sales support person to work with distributors to successfully complete the Prospect Development sequence. Is it up to the job? Can it be described completely so that its “boss,” the machine learner can understand what it is expected to do?

“Just as you wouldn’t hire a human employee without an understanding of how he or she would fit into your organization, you need to think clearly about how an artificial intelligence application will drive actual business results,” wrote Greg Satell, author of Mapping Innovation: A Playbook for Navigating a Disruptive Age, in the Harvard Business Review this month. The metrics identified at this step in the machine learning onboarding process are the equivalent of a job description for the machine learning platform.

A machine learner will analyze the performance of content assets, the sequence in which they are presented, and the messaging that drives views of the content, comparing the results to the expectations and metrics identified during this exercise by sales and marketing leadership. When the system identifies departures from those expectations, the system will seek alternative routes to improved conversion by testing different combinations of content and sales messaging. It sends suggested next steps to the distributor, which they can use or modify (creating more variations the machine learner can analyze), measuring all the results against the goal of speeding prospects to the close.

What does the machine learner need to know about each asset? 

What is the asset about? What is the subject, as well as the keywords, themes, and who or what appears on-screen? Metadata is often missing and may be added in the content management platform. If an asset does not have extensive metadata describing its content, that must be created so that the machine learner is able to test different combinations of assets. For example, if the machine learner knows that a video features a female presenter, it could test that asset with female viewers to see if it converts better. There are myriad combinations of demographic and psychographic factors that can be tested, but only if the asset is thoroughly described in a way the platform can understand and use.

Where is the asset positioned in the current selling sequence? A machine learner may also test different sequences of content to understand if existing content can produce better conversion rates.

Is it the traditional first video shared with a prospect to create interest? Does it depend on any other assets for context, such as a previous video in the sequence? This information is important to preventing the machine learner from rearranging content in a way that doesn’t make sense to the recipient. For example, if your company uses jargon frequently, such as referring to a product using an acronym (e.g., “Comprehensive Weight Magagement is spoken about as “CWM”) it should be explained before it is used in other contexts. Telling the machine learner that one asset must precede another prevents customer confusion because information is presented in the wrong order.

What is the expected outcome of the customer’s engagement with an asset? Is a video or a sales action, like making a call or presenting products at a meeting, expected to increase customer interest? Is there a specific call to action associated with an asset, such as a link to send a message to the distributor who shared it? Is the expected next step after a distributor makes a presentation a purchase, a call being scheduled, or a specific follow-up asset should be shared and viewed? Documenting these expectations provides the machine learner with extensive options to test in different sequences. As long as each expectation is documented, your organization has the basis for a measurement of the response.

What is the expected pace of a complete sales motion? If there is a six-step sequence associated with selling a health product today, for example, are the assets performing satisfactorily as a unit? Is the distributor taking too long to present the steps? Are prospects responding in the expected timeframe? These pace-related signals catalyze machine-generated coaching for the distributor, reminding them to follow-up in the optimal timeframe to make a sale.

In the Gig Economy Group platform, clients can configure specific follow-up questions for the distributor to ask the prospect so that qualitative and quantitative feedback can be captured by humans. Determining whether the prospect more or less interested after seeing an asset or participating in a meeting often requires the sales rep to interpret statements and signals. This ability to interpret the impact of an asset is the distinct advantage in-person sales provide to marketers. Leverage it by developing follow-up questions that can be turned into metrics.

Attribution can be controversial. It is a mistake to lump social and other content together with your sales assets because social content often has different goals. However, as a direct selling company captures more information about its distributors and market, the opportunity to use assets in a different context, for example by adding a personal success story normally shared in social channels to a sales sequence, will emerge. The outcome is a more productive asset library with more applications, which can increase the ROI on every content investment.

With this sales process inventory in place, distributors can be equipped with an evolving selling process that can deliver ongoing improvement in revenue with greater distributor confidence and retention.

Onboarding to Machine Learning: Mapping Sales Processes

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Improving a sales process with machine learning starts with a straightforward assessment of the existing content, including video, audio, text, graphics, and training, a company uses to onboard a new distributor to its policies and practices. These first steps, which set the stage for confident selling by new distributors, are essential to improving sales success during the first two weeks with a new direct selling company. People who close their first sales within 14 days earn an average of 71 percent more than a distributor who takes just four weeks to complete a sale.

Sales and marketing leadership tackling machine learning for the first time need to break their existing onboarding practices and initial selling activities into steps, then organize those steps into collections that are expected to produce a specific result that can be measured. We recommend assembling a map of the onboarding, training, and sales support experience for new distributors, as their immediate success will produce immediate improvement in revenue and profitability results. Tier your product content in terms of 1.) Company overview and welcome programs and content; 2.) Selling materials and programming for distributor use; 3.) Deep product information, such as sales sheets or detailed product knowledge videos.

Break down the first month of distributor experience into:

  • Onboarding: Introduction to the company, its mission, and selling process at the overview level — what you most want your new enrollees to know on Day One and to have internalized by the end of Week One.
  • Prospect Development: This is the first, most important step for a successful sales enablement tool. Rather than explain how to use the contact management tools, get the distributor to work immediately on adding prospects and following up.
  • Product Knowledge Development: Ongoing and frequently updated, product knowledge and product-specific training.
  • Sales Skills Improvement: If there is sales training content that is not product-specific, such as coaching on how to follow up or present at a meeting, these programs will be useful throughout the entire distributor lifetime, not just as they become familiar with the company.

We suggest beginning with a list of all existing content. Write the title of each asset on a sticky note and, on a second note, the goal for the asset, such as “Create a sense of welcoming support” or “Establish product- and lifestyle-claims policy.” Place the two sticky notes, asset and goal for the asset side by side. Examine all the content related to onboarding to see if there are multiple assets seeking to achieve the same outcome.

As common goals are identified, cluster the content assets by the expected outcome. It is likely there will be several assets that drive to the same distributor goal, and these variations are natural places for a machine learning content system to start testing to see which content assets are most effective.

Introductory content, such as a generic welcome message and overviews of the company, should be separated from practical how-to content related to using tools and services offered by the company to refine distributor sales skills. The latter training content will distract distributors from mission-centric learning. For example, most direct selling systems begin with a series of introductory videos about the company, its products, and how the distributor can start to work its selling process. These videos set the stage for future training, but they have a narrow set of goals: To build confidence in the distributor that they’ve made the right choice of product or service to sell, that the company is reliable and supportive of their success. This is essential for winning younger distributors’ loyalty.

With mission- and policy-centric content organized into the first category, the next step is to organize each of your sales task workflows for use by the machine learning platform.

Each days’ distributor training activities during the first two weeks must have a goal, such as confirmation that the new distributor understands the basic value proposition and mission of the company or that they enter and start communicating with prospects. And each day’s activities should contribute to the next day’s goals — if on Day One, the distributor enters five contacts, Day Two should include follow-up activities and content that help convert those leads to a call, presentation, or online meeting.

Look for multi-day processes, such as prospect development and determine whether multiple assets address the same steps and issues. These are convenient reference points when thinking about how to shorten and improve onboarding programming, which can produce immediate improvements in distributor success. Sales process steps in a “What’s Next” machine learning tool allow the distributor to focus on doing sales work instead of learning how to use tools.

Once the Welcome and Onboarding workflows are complete and redundant content identified for testing, the organization of product knowledge and sales skills coaching content if there is any in the current asset library. These are content categories that can be populated over time, as well as licensed from training providers for integration with sales coaching machine learners, which can target sales training based on the distributor’s sales challenges. For instance, if they consistently add contacts, get meetings, but don’t close, the tool can direct the distributor to training videos about closing, getting commitments, and handling objections.

With a smart platform in place, a variety of training programs can be added to address your network’s training needs and to address individual distributor challenges. In the next installment, we’ll explore attribution modeling for machine optimization of each step in the sales process.

Getting Started With Machine Learning for Sales Content

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Human insight and inspiration are the basis of solidly profitable applications of machine learning to sales processes. LifeVantage, which recently launched a Gig Economy Group-based sales app for its distributors, improved its sales and marketing alignment as part of their onboarding to machine learning.

The GEG process builds on five principles that ensure clients onboard to improved, personalized sales experiences:

  1. Attribution analysis provides useful metrics for assessing content and messaging.
  2. Design to deliver “What’s Next” automatically, offering distributors the choice to use or refuse the machine suggestion.
  3. Make the process repeatable and scalable while supporting testable content and messaging variations.
  4. Deliver real-time leading indicators to management to facilitate their own content production decisions instead of blindly following machine recommendations.
  5. Provide a proactive view of the business from Day One.

We began the LifeVantage process using the company’s existing video, audio, and PDF-based content assets. Those assets served as the foundation of a machine learning platform. Assigning expected outcomes for each of these assets, whether LifeVantage management believed the asset moves a prospect toward purchasing, provided the GEG analytics system to identify opportunities for optimization. Each surprising outcome lets the machine learner choose alternative steps and test their efficacy in the funnel.

The teams assessed content provided to distributors as they join the organization, and consumer marketing content. Every aspect of the interactions between LifeVantage sellers and customers were examined and cataloged. With this inventory and a list of expected outcomes at each step, a machine learner can look at every interaction to identify what content works best, which messaging spurs prospect action, and the optimal order for delivering marketing content to customers.

Having innovated early with several technology vendors to develop four mobile apps, LifeVantage found its new distributor onboarding had become fragmented across several applications. Each app addressed different aspects of learning the LifeVantage Way, the selling process, and ongoing training. The company realized its distributors needed a single point of contact with LifeVantage information and its backend sales management systems. Simply providing training through streaming videos, audio programs, and coaching by sponsors would not be sufficient to keep a new distributor engaged if their early investment of time in LifeVantage did not convert to sales.

Results, Before The Machine Kicks In

The LifeVantage app for iPhone and Android connects distributors to the company’s media catalog and sales platform to provide next-step guidance for each phase of the selling process. Building on its established training and invaluable guidance from its most successful distributors, LifeVantage analyzed each step in its selling motion to create a machine learner capable of recognizing the salesperson’s progress in training, achieving product competence, and how each relationship they are working is progressing against network-wide benchmarks.

It was clear from the start a mobile-first strategy was essential. In many cases, distributors used paper-based sales management tools rather than PC applications. iPhone and Android versions of the app became priorities for the team, who moved to the design stage with a challenge: How to make the seller’s success as simple and pleasing as an Uber rider or driver’s experience.

A significant finding in early surveying and interviews centered on the design on framing seller’s choices. Distributors did not want to be told they have only one option, which they must follow the instructions given precisely. Instead, they sometimes want to skip sharing a video or time the gathering of feedback differently than established LifeVantage practice. For newcomers to LifeVantage sales, spontaneity in communication reinforced their sense of confidence. The team had to allow users plenty of flexibility. Experienced distributors moving from other network marketing companies were less inclined to watch training, but eager to get to work.

Through internal discussion and continuing interviews with experienced distributors, the team settled on critical metrics to change with the app that focused on novice sellers. While the first version of the LifeVantage app does offer services for long-term distributors, the newest seller is the next source of growth at the company. The selling steps and events that lead to initial success, such as time to first contact added and messaged, the frequency and success of the new distributor’s meetings, and speedy progress toward the first sale and progress up the compensation ladder became central to the project.

Customization At the Design Stage

Rapid development demanded continuous collaboration between the design and development teams during the onboarding and launch process. The initial distributor interviews produced a design the team decided required too much tool knowledge on the user’s part. The needed to know steps when adding a contact or creating a meeting, for instance, gets in the way of completing the task. A key decision resulted: Each action card that called for a distributor task should open the workspace where the work takes will be performed and, on completion, acknowledge the progress made. Sellers want feedback from their app about their progress.

App performance, too, presented challenges. Machine learning is an evolving computationally intensive technology. Building in a robust development environment on proven cloud systems was essential to fast responses to user input in the LifeVantage app. Feedback from distributors came fast and frequently, providing many signals about where to prioritize early investments.

The internal knowledge that had seemed concrete turned out to be merely intuitive guesswork in some cases. App usage demonstrated that new distributors wanted to get to work by starting to communicate instead of going through extensive training. Consequently, training sequences were shortened and selling steps moved to the “top of the deck” of the new user’s experience. Early usage showed distributors gravitate to creating new business. Consequently, LifeVantage recast much of its extensive media catalog as daily training material presented contextually while the distributor is performing a related task. When related to an immediate sales challenge, such as gaining commitments, LifeVantage’s training content proved even more effective.

Designers and developers worked closely to move the beta through seven release cycles, each shared with the beta community. Additionally, 20 top LifeVantage distributors and a cadre of 20 newly registered distributors were engaged to give weekly feedback. The result was hundreds of changes to the functionality and design captured over three months that would have taken a year to collect through traditional channels.

The first general release of the LifeVantage app delivered a simple-to-use tool for learning, selling, and supporting customer and distributor network relationships. It integrates the LifeVantage’s Media Library, Contact Management, Meeting and Feedback Management, as well as providing business performance information that assists sponsors in training their distributor networks. From first contact and entry of personal data into the app, through media sharing, it prompts distributors to make calls and send messages that capture prospect feedback. Based on what the prospect does, the app selects from a variety of optional next steps, such as triggering a distributor enrollment or shared cart with a potential buyer.